Here Are Some Scientific Arguments James Damore Has Yet to Respond To

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In The Wall Street Journal this afternoon, James Damore, the Google engineer fired for his bad memo, explained why he was fired from Google. I’ll save you some time: He was, according to him, fired from Google because the company is a liberal haven whose employees are afraid to leave their comfort zones. It uses the term “echo chamber” no less than five times.

Damore believes that, “I committed heresy against the Google creed by stating that not all disparities between men and women that we see in the world are the result of discriminatory treatment.”

He wonders, “How did Google, the company that hires the smartest people in the world, become so ideologically driven and intolerant of scientific debate and reasoned argument?”

But shockingly, or maybe not shockingly, Damore seems uninterested, or unable, in responding to the scientific debate and reasoned argument his memo engendered. Since his memo was published, plenty of people have responded to it with reasoned arguments that cite scientific and academic findings; he may be annoyed with Google, but surely he has time to participate in the scientific debate he so eagerly seeks?

While James is raging against the dearth of scientific argument on alt-right YouTube and the WSJ op-ed section, here are a few scientists that he hasn’t yet found the time to respond to.

His implicit model is that cognitive traits must be either biological (i.e. innate, natural, and unchangeable) or non-biological (i.e., learned by a blank slate). This nature versus nurture dichotomy is completely outdated and nobody in the field takes it seriously. Rather, modern research is based on the much more biologically reasonable view that neurological traits develop over time under the simultaneous influence of epigenetic, genetic and environmental influences. Everything about humans involves both nature and nurture.

Rosalind C. Barnett and Caryl Rivers, who have “studied gender and STEM for 25 years,” in Recode:

Several major books have debunked the idea of important brain differences between the sexes. Lise Eliot, associate professor in the Department of Neuroscience at the Chicago Medical School, did an exhaustive review of the scientific literature on human brains from birth to adolescence. She concluded, in her book “Pink Brain, Blue Brain,” that there is “surprisingly little solid evidence of sex differences in children’s brains.”

Rebecca Jordan-Young, a sociomedical scientist and professor at Barnard College, also rejects the notion that there are pink and blue brains, and that the differing organization of female and male brains is the key to behavior. In her book “Brain Storm: The Flaws in the Science of Sex Differences,” she says that this narrative misunderstands the complexities of biology and the dynamic nature of brain development.

Geoffrey Miller, an evolutionary psychology professor at the University of New Mexico, in Quillette:

American businesses also have to face the fact that the demographic differences that make diversity useful will not lead to equality of outcome in every hire or promotion. Equality or diversity: choose one. In my opinion, given that sex differences are so well-established, and the sexes have such intricately complementary quirks, it may often be sensible, in purely practical business terms, to aim for more equal sex ratios in many corporate teams, projects, and divisions.

Still, it is not clear to me how such sex differences are relevant to the Google workplace. And even if sex differences in negative emotionality were relevant to occupational performance at Google (e.g., not being able to handle stressful assignments), the size of these negative emotion sex differences is not very large (typically, ranging between “small” to “moderate” in statistical effect size terminology; accounting for perhaps 10% of the variance). Using someone’s biological sex to essentialize an entire group of people’s personality is like surgically operating with an axe. Not precise enough to do much good, probably will cause a lot of harm. Moreover, men are more emotional than women in certain ways, too. Sex differences in emotion depend on the type of emotion, how it is measured, where it is expressed, when it is expressed, and lots of other contextual factors. How this all fits into the Google workplace is unclear to me. But perhaps it does.

In the end, focusing the conversation on the minutiae of the scientific claims in the manifesto is a red herring. Regardless of whether biological differences exist, there is no shortage of glaring evidence, in individual stories and in scientific studies, that women in tech experience bias and a general lack of a welcoming environment, as do underrepresented minorities. Until these problems are resolved, our focus should be on remedying that injustice. After that work is complete, we can reassess whether small effect size biological components have anything to do with lingering imbalances.

Dan Davies, equity analyst (okay, so not a scientist, but a thoughtful statistical response), writing on Crooked Timber:

The true underlying distributions would be useful if Google’s hiring process was to select people at random from the population, put them through a standard test of the single “quality” variable of interest, then take the ones who passed the test and discard the ones who failed. As a description of how recruitment processes don’t work, this is pretty spot on. Google (like any other company — I first started making this argument in the 1990s when McKinsey were publishing their incredibly influential, amazingly wrong and massively destructive “War For Talent” series) fills jobs by advertising for vacancies or encouraging through word of mouth and recruiters, using interview questions and tests which might have unknown biases, and recruiting people for their suitability for the roles currently vacant (which is not the same thing as “quality” because companies change all the time but keep the same employees. Each one of these stages is enough of a departure from the random sampling model to mean that the population distributions are not relevant.